Systems and methods for clustering items associated with interactions

ABSTRACT

Systems, methods, and non-transitory computer readable media configured to generate session information based on information regarding items of a plurality of item types associated with interactions performed by active users of a social networking system. A graph is generated based on the session information. At least a first item of the items is assigned to a cluster based on similarity between the item and the cluster. The cluster is provided to a recommender system to facilitate selection of relevant information for potential presentation to a user.

FIELD OF THE INVENTION

The present technology relates to the field of content provision. Moreparticularly, the present technology relates to techniques forclustering items associated with interactions of a social networkingsystem.

BACKGROUND

Today, people often utilize computing devices (or systems) for a widevariety of purposes. Users can use their computing devices to, forexample, interact with one another, access content, share content, andcreate content. In some cases, content items can include postings frommembers of a social network. The postings may include text and mediacontent items, such as images, videos, and audio. The postings may bepublished to the social network for consumption by others.

Under conventional approaches, a user may navigate to or be presentedwith various content items in a social network. The content items cancome from pages associated with members of the social network. In someinstances, the content items may be of high interest to the user. If theuser expresses interest in a particular content item, the social networkmay attempt, based on the content item, to provide to the useradditional content items that likewise would be of high interest to theuser. Provision of additional content items that are of high interest tothe user enhances user experience and can help realize the fullpotential of the social network. Unfortunately, attempts to provide suchadditional content items and to maintain a high level of interest fromthe user often fail.

SUMMARY

Various embodiments of the present disclosure can include systems,methods, and non-transitory computer readable media configured togenerate session information based on information regarding items of aplurality of item types associated with interactions performed by activeusers of a social networking system. A graph is generated based on thesession information. At least a first item of the items is assigned to acluster based on similarity between the item and the cluster. Thecluster is provided to a recommender system to facilitate selection ofrelevant information for potential presentation to a user.

In an embodiment, the plurality of item types include at least one ofother users, profiles, groups, pages, hashtags, topics, links, photos,and search terms.

In an embodiment, the session information is based on a user or a post.

In an embodiment, the information regarding items of a plurality of itemtypes is limited by at least one of a threshold number of the activeusers and a threshold number of interactions associated with the activeusers.

In an embodiment, noise in the session information is removed based onat least one of spending less than a first threshold amount of time foreach interaction, activity for more than a second threshold amount oftime for a session, and engagement in a cycle of interaction.

In an embodiment, edges of a node of the graph are rebalanced tooptimize memory usage in storage of the graph.

In an embodiment, the rebalancing edges of a node of the graph furthercomprises multiplying transition probabilities between the node and eachof connected nodes by a constant value when the total count of edges forthe node satisfies a threshold value.

In an embodiment, the constant value is based on a threshold valuedivided by a transition probability of a k-th largest connected node byweight when the transition probability is greater than the thresholdvalue.

In an embodiment, a similarity score between the first item and arepresentative item of the cluster is determined. It is determinedwhether the similarity score satisfies a threshold value.

In an embodiment, bi-directional agreement between the first item and arepresentative item of the cluster interact and internal cohesion of thecluster are determined.

It should be appreciated that many other features, applications,embodiments, and/or variations of the disclosed technology will beapparent from the accompanying drawings and from the following detaileddescription. Additional and/or alternative implementations of thestructures, systems, non-transitory computer readable media, and methodsdescribed herein can be employed without departing from the principlesof the disclosed technology.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example clustering system, according to anembodiment of the present disclosure.

FIG. 2 illustrates an example joiner module, according to an embodimentof the present disclosure.

FIG. 3 illustrates an example graph module, according to an embodimentof the present disclosure.

FIG. 4 illustrates a first example method, according to an embodiment ofthe present disclosure.

FIG. 5 illustrates a second example method, according to an embodimentof the present disclosure.

FIG. 6 illustrates a network diagram of an example system that can beutilized in various scenarios, according to an embodiment of the presentdisclosure.

FIG. 7 illustrates an example of a computer system that can be utilizedin various scenarios, according to an embodiment of the presentdisclosure.

The figures depict various embodiments of the disclosed technology forpurposes of illustration only, wherein the figures use like referencenumerals to identify like elements. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated in the figures can be employedwithout departing from the principles of the disclosed technologydescribed herein.

DETAILED DESCRIPTION

Entity Clustering

People use computing devices (or systems) for a wide variety ofpurposes. Computing devices can provide different kinds offunctionality. Users can utilize their computing devices to produceinformation, access information, and share information. In some cases,users can use their computing devices to generate and publish contentpostings. Content can include any combination of content types, such astext, images, videos, and audio. The content can be shared forconsumption by others through a social networking system. The contentcan be shared in a variety of formats, such as pages of or posts to thesocial networking system.

The conventional presentation of content can entail many disadvantages.The social networking system may attempt to identify additional contentthat is of interest to the user. However, when presented to the user,the additional content may not be desirable to the user because they arenot well matched with the interests of the user. In such circumstances,the user can be provided with content that the user deems unfamiliar,irrelevant, or worse. As a result, the user experience of the socialnetworking system can suffer.

An improved approach to the presentation of additional content overcomesthe foregoing and other disadvantages associated with conventionalapproaches. The present disclosure is a real time (or near real time)system that can be used to improve recommender systems that presentcontent to a user. For example, with respect to a user, the presentdisclosure can collect the items (or entities) users have interacted(engaged) with in sequence. Noise reduction techniques can be applied tothe collection of the items. Sequences can be aggregated together toform a graph of items with edge weights reflecting a count oftransitions between two items. The edge weights in the graph can providea transition probability from one item to another item. The edge weightscan be rebalanced to optimize memory usage and to address rapid edgedecay in certain circumstances. The transition probabilities can be usedfor clustering of items that can be used in real time (or near realtime) by recommender systems to provide additional, relevant informationto the user.

FIG. 1 illustrates an example system 100 including an example clusteringmodule 102 configured to facilitate the determination of additionalcontent by recommender systems 104 to present to a user of a socialnetworking system, according to an embodiment of the present disclosure.The recommender systems 104, including user-to-item recommender systemsand item-to-item recommender systems, can include one or more types oftechniques for providing relevant information to the user in a socialnetworking system. In some instances, the example system 100 can alsoinclude at least one data store 116.

The clustering module 102 can include a tailer module 108, a joinermodule 110, and a graph module 112. The components (e.g., modules,elements, etc.) shown in this figure and all figures herein areexemplary only, and other implementations may include additional, fewer,integrated, or different components. Some components may not be shown soas not to obscure relevant details.

In some embodiments, the clustering module 102 can be implemented, inpart or in whole, as software, hardware, or any combination thereof. Ingeneral, a module as discussed herein can be associated with software,hardware, or any combination thereof. In some implementations, one ormore functions, tasks, and/or operations of modules can be carried outor performed by software routines, software processes, hardware, and/orany combination thereof. In some cases, the clustering module 102 can beimplemented, in part or in whole, as software running on one or morecomputing devices or systems. In some embodiments, the clustering module102 and data managed by the clustering module 102, such as graph data,can be distributed over an array of computing devices, such as servers,and sharding techniques can be used to localize related or connecteddata (e.g., connected nodes and associated edges in the graph) in onecomputing device to optimize speed and minimize latency. In someinstances, the clustering module 102 can, in part or in whole, beimplemented within or configured to operate in conjunction with a socialnetworking system (or service), such as the social networking system 630of FIG. 6. It should be understood that many variations are possible.

The tailer module 108 can access data relating to interaction(engagement) with the social networking system or other websites forwhich the social networking system acts as a platform. The data can beprovided by a system that interfaces with front end servers to track inreal time (or near real time) all interactions with items (entities).Items can include, for example, other users, groups, pages, hashtags,topics, links, photos, search terms, etc. Interactions can include, forexample, production, clicking, viewing, navigating, etc. As someexamples, interactions can relate to page visits, page likes, profilefollows, URL shares, hashtag authoring, topic authoring, etc. The datacan be log entries in the form of messages. In particular, the tailermodule 108 can receive desired categories of the messages and extractinformation about interactions. The information can include, forexample, identity of the user performing the interaction (User-Id),identity of an item with which user is interacting (Item-Id), the typeof the item, the time of the interaction, and the location of theinteraction. This information can be encoded in a message forconsumption by the joiner module 110. The tailer module 108 can queuemessages for consumption by the joiner module 110 in afirst-in-first-out manner.

The joiner module 110 can collect interactions of active users. Thejoiner module 110 can maintain a time and space efficient indexed (orhash) table designed to support concurrent writes. In one example, thetable can be keyed by a user (or associated User-Id). Each User-Id valuecan contain a sequence of items with which the user has interacted. Thesequence can be referred to as a session. The items in the session canbe ordered by recency of interaction with each item. In another example,the table can be keyed by a post (or associated post-ID). The joinermodule 110 can maintain memory efficiency by limiting the size of theindexed table and the number of items with which the user hasinteracted. The joiner module 110 also can identify sources of noise andaccordingly ignore or eliminate associated interactions. The joinermodule 110 is discussed in more detail herein.

The graph module 112 can manage a multi-level graph reflecting userinteraction with items and track transitions between the same type ofitems (homogeneous) and between different types of items(heterogeneous). The graph can be implemented as a time and spaceefficient indexed table designed to support concurrent writes. The graphmodule 112 can track transitions by a variety of metadata, such as bytype of item and by location. The graph module 112 can perform arebalancing technique relating to transition (edge) counts to optimizememory efficiency and to address rapid decay in certain circumstances.The graph module 112 can generate similarity scores based on transitionsto facilitate the determination of clusters of items. The graph module112 can generate clusters of the items according to the similarityscores and provide the clusters to the recommender systems 104. Thegraph module 302 is discussed in more detail herein.

The recommender systems 104 can include systems that select content forpotential presentation to a user relating to, for example, a trendingtopic, page recommendations, group recommendations, searchrecommendations, and the like. The recommender systems 104 can be usedin connection with underlying techniques that monitor activities on thesocial networking system. Such techniques can include, for example,co-visitation, co-interaction, co-production, and co-liking. Therecommender systems 104 can provide relevant information for potentialpresentation to a user based on the clusters provided by the graphmodule 112.

The data store 116 can be configured to store and maintain various typesof data associated with the clustering system 102 and the socialnetworking system. The information associated with the social networkingsystem can include data about users, social connections, socialinteractions, locations, geo-fenced areas, maps, places, events, groups,posts, communications, content, account settings, privacy settings, andvarious other types of data. In some implementations, the data store 116can store information associated with users, such as user identifiers,user information, user specified settings, content produced by users,and various other types of user data. As shown in the example system100, the clustering module 102 can be configured to communicate and/oroperate with the data store 116.

FIG. 2 illustrates an example joiner module 202, according to anembodiment of the present disclosure. In some embodiments, the joinermodule 110 can be implemented by the joiner module 202. As shown in theexample of FIG. 2, the joiner module 202 can include a session module204 and a noise reduction module 206.

The session module 204 can join relevant interaction data to createsessions maintained as indexed tables. Each session may include allrelevant interactions with items based on a key. The session module 204can order the items based on recency. The items in a session may includethe same type of items or may include different types of items. Forexample, the session module 204 can collect interactions based on a userin a session. In this regard, the user can visit a page, fan the page,post regarding the page, visit a group, and perform other interactions.These interactions can be included in a session associated with theuser. As another example, the session module 204 can collectinteractions based on an identification of a post (Post-ID). In thisregard, if a user published a post having a hashtag and a URL, thehashtag and the URL can be joined on the Post-ID and included in asession associated with the Post-ID. The session module 204 can maintainsessions in a queue for provision to the graph module 112.

The session module 204 can maintain memory efficiency through varioustechniques. For example, the session module 204 can limit the userstracked to only active users. As another example, the session module 204can limit the number of active users tracked to a selected maximumnumber of active users. When a new active user surfaces, the oldestactive user may be dropped from tracking. In addition, the sessionmodule 204 can limit the number of interactions of each active user thatare tracked to a maximum number of interactions. A circular list(counter) may be applied to limit the number of interactions to themaximum number.

The noise reduction module 206 can identify sources of noise andaccordingly ignore or eliminate associated interactions according to avariety of techniques. The sources of noise can be reflected ininteraction data and undesirably impact probabilities of transition fornodes and related clustering. The sources of noise can be automatedthird party processes responsible for spam, malware, or maliciousactivity on the social networking system.

In one technique, the noise reduction module 206 can determine whether auser is spending less than a threshold amount of time for eachinteraction (e.g., page visit). Spending relatively small amounts oftime on interactions can be indicative of automated behavior and a thirdparty process designed to scrape content from some or all of the pagesof the social networking system. The threshold amount of time for eachinteraction can be based on a type of the item on which the interactionwas performed. The threshold amount of time for each interaction can beselected by an administrator of the social networking system. Wheninteractions do not equal or exceed the threshold amount of time, thenoise reduction module 206 can blacklist the User-ID associated with theinteractions. All interactions associated with the User-ID can beignored by the joiner module 110 and not used in updating the graph.

In another technique, the noise reduction module 206 can determinewhether a user is active for more than a threshold amount of time foreach session of interactions. Spending relatively large amounts of timefor each session can be indicative of automated behavior and usage ofthe social networking system that is not intended. The threshold amountof time for each session can be selected by an administrator of thesocial networking system. When a session of a user equals or exceeds thethreshold amount of time for each session, the noise reduction module206 can black list the User-ID associated with the interactions. Allinteractions associated with the User-ID can be ignored by the joinermodule 110 and not used in updating the graph.

In yet another technique, the noise reduction module 206 can determinewhether a user has engaged in a cycle (pattern) of interaction. Cyclesof interaction can be indicative of automated behavior and usage of thewebsite of the social networking system that is not intended. The noisereduction module 206 can select a predetermined number of interactions(e.g., page visits) of a user. The predetermined number of interactionscan be selected by an administrator of the social networking system. Forexample, the noise reduction module 206 can determine the page IDs of aselected number of consecutive pages that were visited by the user. Thenoise reduction module 206 can analyze the historical page visitsassociated with the user. If the noise reduction module 206 detects theuser visited the same consecutive pages on another occasion, the noisereduction module can black list the User-ID associated with theinteractions. All interactions associated with the User-ID can beignored by the joiner module 110 and not used in updating the graph.

-   -   FIG. 3 illustrates an example graph module 302, according to an        embodiment of the present disclosure. The graph module 302 can        be configured to manage a graph reflecting interaction with        items and track transitions between items. In some embodiments,        the graph module 112 can be implemented by the graph module 302.        The graph module 302 can include, a generation module 304, a        rebalancing module 306, and a clustering module 308.

The generation module 304 is configured to build a graph representingthe items and interactions. The items are represented by nodes in thegraph and the transitions between items are represented by edges in thegraph. The generation module 304 can continuously update edges in thegraph. The edges of a node can be incoming, outgoing, or bi-directional.Each edge can be added to the graph by a count of integer values. Thegraph can be a multi-level graph reflecting interaction between the sametype of items (homogeneous) and between different types of items(heterogeneous). The generation module 304 can track transitions by avariety of metadata, such as by type of item and by location.

The graph can reflect a wide array of interactions involving differentitems. For example, if a user visited a first page, and then visited asecond page, and then visited a profile, and then visited a group, thegeneration module 304 can appropriately reflect the items andinteractions in the graph. A node can be created for each of the firstpage, the second page, the profile, and the group. Edges, ortransitions, can be created among the nodes. In particular, a transitionfrom the first page to the second page can be created, a transitionbetween the second page to the profile can be created, and a transitionfrom the profile to the group. For a multitude of users whoseinteractions may involve various interactions with the first page, thesecond page, the profile, and the group, corresponding transitions maybe created among the associated nodes. Not all interactions with thefirst page, the second page, the profile, and the group may proceed inthe stated sequence. For instance, some users may visit the second page,and then visit the first page, and then visit the profile, and thenvisit the group. In another instance, some users may visit the secondpage, and then visit the group, and then visit the first page, but notvisit the first page.

As another example, if a post (e.g., status update, comment) ispublished, the text of the post may result in the generation of, forexample, topic tags, hashtags, and URLs. The generation module 304 canappropriately reflect the topic tags, the hashtags, and the URLs in thegraph. Nodes and transitions can be created for the topic tags, thehashtags, and the URLs.

The generation module 304 can generate transitions based on thedifferent interactions and generated relationships between items. Over athreshold number of users and generated relationships over time, thetransitions, when aggregated, can converge on certain ratios ortransition probabilities with respect to one another. The transitionprobabilities can be used to cluster items and facilitate the selectionof relevant information by the recommender systems 104.

The rebalancing module 306 can manage edges in the graph to optimize theuse of memory for storage of the graph and to address rapid decay incertain circumstances involving, for example, trending. The rebalancingmodule 306 can apply a decay (reduction) to the number of edges betweena node and its connected nodes in a graph in certain circumstances. Insome embodiments, the rebalancing module 306 can count all of the edgesfor the node. If the total number of edges for the node is greater thanor equal to a threshold total number of edges, the rebalancing module306 can decay the total number of edges.

The decay can in whole or in part maintain the probabilities oftransition reflected in the edges prior to the decay. The probability oftransition associated with a node and a particular connected node can berepresented by the count of transitions between the node and theparticular connected node (i.e., weight of the edges between the nodeand the particular connected node) divided by the sum of the count oftransitions between the node and all of the connected nodes (i.e.,weight of all edges between the node and all connected nodes). Therebalancing module 306 can determine a constant value between zero andone. Each count of transitions between the node and a connected node canbe multiplied by a constant value, such as a fraction. The product ofthe count of transitions between the node and a connected node and theconstant value can generate a weight, which is rounded down to thenearest integer. Any connected node associated with a weight equal tozero can be removed from the graph.

In some embodiments, undesirable rapid edge decay can be addressed. Suchrapid decay can occur in connection with trending items. A constantvalue can be selected as a function of transition probabilities topreserve the top k transition probabilities above a threshold valuethrough edge decay. The threshold value can be a minimum percentage ofthe weight of the edges of all connected nodes. When the transitionprobability of the k-th largest connected node by weight among theconnected nodes is greater than the threshold value, the constant valueis equal to the threshold value divided by the transition probability ofthe k-th largest connected node by weight among the connected nodes. Theconstant value can be multiplied by each of the transition probabilitiesassociated with the node and each of the connected nodes. The product ofthe constant value and each of the transition probabilities can generatea new weight between the node and each of the connected nodes. When thetransition probability of the k-th largest connected node by weightamong the connected nodes is not greater than the threshold value, theconstant value can be any default value.

The clustering module 308 can generate similarity scores based ontransitions to facilitate the determination of clusters of items. Theclustering module 308 can cluster items based on similarity scores andthe clusters can be provided to the recommender systems 104 for theselection and presentation of relevant information to users. Theclusters of items can include items of the same type and items ofdifferent type. The similarity scores can reflect a degree of similaritybetween two items based on the edges connecting the items.

With respect to items of the same type, the clustering module 308 cangenerate clusters based on the similarity scores of the items. Thesimilarity score between an item and a particular, connected item can berepresented by the count of transitions between a node associated withthe item and a node associated with the particular, connected item(i.e., weight of the particular node) divided by the sum of the count oftransitions between the node and all of the nodes connected to the node(i.e., weight of all connected nodes). The clustering module 308 alsocan determine a similarity score between an item and a cluster. Todetermine the similarity score between the item and the cluster, theclustering module 308 can select any item in the cluster or an item inthe cluster that is representative of the items in the cluster. Thesimilarity score between the item and the cluster can be based on thesimilarity score between the item and the item in the cluster.

In some embodiments, the similarity score can be determined in a mannerthat applies a threshold value to account for random transitions betweenitems that are not significant in the determination of clusters ofitems. The threshold value may be a suitable percentage of the totalnumber of transitions of the item. When the total count of transitionsbetween the item and another item connected to the item is less than orequal to the threshold value, then the similarity score between the itemand the other item can be set to zero. A similarity score of zeroindicates that two items are not related and that the two items shouldnot be included in the same cluster.

Two items can be assigned to a cluster based on the similarity scoresbetween the two items. When the similarity score between the two itemsis greater than or equal to a threshold value, the two items can beincluded in the same cluster. Likewise, when the similarity scorebetween an item and a cluster is greater than or equal to the thresholdvalue, the item can be determined to be sufficiently similar to thecluster to warrant assignment of the item in the cluster.

With respect to items of different type, clusters can be generated basedon bi-directional agreement between clusters and internal cohesion ofthe clusters. For example, assume a set of items of both a first typeand a second type and the existence of a first cluster. Assume furtherthat a first item in the set is of the first type and that the firstitem is representative of the first cluster. The clustering module 108can determine whether a second item of the second type in the set is tobe included in the first cluster or included as part of a new cluster.The determination can be based on the extent to which the second iteminteracts (e.g., co-visits) with the first item in a bi-directionalmanner and on the internal cohesion of the first cluster. If the seconditem interacts with the first item in a bi-directional manner to anextent that is greater than or equal to a threshold value, suchinteraction with the first item is greater than interaction with otherclusters, and internal cohesion of the first cluster satisfies athreshold value, then the second item can be included in the cluster ofthe first item. Otherwise, the second item is not included in thecluster including the first item. In this case, the second item can beincluded in a new second cluster.

The recommender systems 104 can be provided with clusters associatedwith an item with which the user is interacting. Based on the clusters,the recommender systems 104 can identify additional items of relevantinformation for potential presentation to the user. The recommendersystems 104 may select the additional items based on the transitionprobabilities (or similarity scores) with respect to the node associatedwith the item. For example, the recommender systems 104 may select athreshold number of connected items having the highest transitionprobabilities with respect to an item for potential provision to theuser.

FIG. 4 illustrates a first example method 400, according to anembodiment of the present disclosure. It should be appreciated thatthere can be additional, fewer, or alternative steps performed insimilar or alternative orders, or in parallel, within the scope of thevarious embodiments unless otherwise stated.

At block 402, the method 400 can generate session information based oninformation regarding items of a plurality of item types associated withinteractions performed by active users of a social networking system. Atblock 404, the method 400 can generate a graph based on the sessioninformation. At block 406, the method 400 can assign at least a firstitem of the items to a cluster based on similarity between the item andthe cluster. At block 408, the method 400 can provide the cluster to arecommender system to facilitate selection of relevant information forpotential presentation to a user. Other suitable techniques arepossible.

FIG. 5 illustrates a second example method 500, according to anembodiment of the present disclosure. It should be appreciated thatthere can be additional, fewer, or alternative steps performed insimilar or alternative orders, or in parallel, within the scope of thevarious embodiments unless otherwise stated.

At block 502, the method 500 can generate session information based oninformation regarding items of a plurality of item types associated withinteractions performed by active users of a social networking system. Atblock 504, the method 500 can limit the information regarding items of aplurality of item types by at least one of a number of the active usersand a number of interactions associated with the active users. At block506, the method 500 can remove noise in the session information based onat least one of spending less than a first threshold amount of time foreach interaction, activity for more than a second threshold amount oftime for a session, and engagement in a cycle of interaction. At block508, the method 500 can generate a graph based on the sessioninformation. At block 510, the method 500 can rebalance edges of a nodeof the graph to optimize memory usage in storage of the graph. At block512, the method 500 can determine a similarity score between a firstitem of the items and a representative item of a cluster and determinewhether the similarity score satisfies a threshold value. At block 514,the method 500 can assign the first item of the items to the clusterbased on similarity between the item and the cluster. At block 516, themethod 500 can provide the cluster to a recommender system to facilitateselection of relevant information for potential presentation to a user.Other suitable techniques are possible.

Social Networking System—Example Implementation

FIG. 6 illustrates a network diagram of an example system 600 that canbe utilized in various scenarios, in accordance with an embodiment ofthe present disclosure. The system 600 includes one or more user devices610, one or more external systems 620, a social networking system (orservice) 630, and a network 650. In an embodiment, the social networkingservice, provider, and/or system discussed in connection with theembodiments described above may be implemented as the social networkingsystem 630. For purposes of illustration, the embodiment of the system600, shown by FIG. 6, includes a single external system 620 and a singleuser device 610. However, in other embodiments, the system 600 mayinclude more user devices 610 and/or more external systems 620. Incertain embodiments, the social networking system 630 is operated by asocial network provider, whereas the external systems 620 are separatefrom the social networking system 630 in that they may be operated bydifferent entities. In various embodiments, however, the socialnetworking system 630 and the external systems 620 operate inconjunction to provide social networking services to users (or members)of the social networking system 630. In this sense, the socialnetworking system 630 provides a platform or backbone, which othersystems, such as external systems 620, may use to provide socialnetworking services and functionalities to users across the Internet.

The user device 610 comprises one or more computing devices that canreceive input from a user and transmit and receive data via the network650. In one embodiment, the user device 610 is a conventional computersystem executing, for example, a Microsoft Windows compatible operatingsystem (OS), Apple OS X, and/or a Linux distribution. In anotherembodiment, the user device 610 can be a device having computerfunctionality, such as a smart-phone, a tablet, a personal digitalassistant (PDA), a mobile telephone, etc. The user device 610 isconfigured to communicate via the network 650. The user device 610 canexecute an application, for example, a browser application that allows auser of the user device 610 to interact with the social networkingsystem 630. In another embodiment, the user device 610 interacts withthe social networking system 630 through an application programminginterface (API) provided by the native operating system of the userdevice 610, such as iOS and ANDROID. The user device 610 is configuredto communicate with the external system 620 and the social networkingsystem 630 via the network 650, which may comprise any combination oflocal area and/or wide area networks, using wired and/or wirelesscommunication systems.

In one embodiment, the network 650 uses standard communicationstechnologies and protocols. Thus, the network 650 can include linksusing technologies such as Ethernet, 702.11, worldwide interoperabilityfor microwave access (WiMAX), 3G, 4G, CDMA, GSM, LTE, digital subscriberline (DSL), etc. Similarly, the networking protocols used on the network650 can include multiprotocol label switching (MPLS), transmissioncontrol protocol/Internet protocol (TCP/IP), User Datagram Protocol(UDP), hypertext transport protocol (HTTP), simple mail transferprotocol (SMTP), file transfer protocol (FTP), and the like. The dataexchanged over the network 650 can be represented using technologiesand/or formats including hypertext markup language (HTML) and extensiblemarkup language (XML). In addition, all or some links can be encryptedusing conventional encryption technologies such as secure sockets layer(SSL), transport layer security (TLS), and Internet Protocol security(IPsec).

In one embodiment, the user device 610 may display content from theexternal system 620 and/or from the social networking system 630 byprocessing a markup language document 614 received from the externalsystem 620 and from the social networking system 630 using a browserapplication 612. The markup language document 614 identifies content andone or more instructions describing formatting or presentation of thecontent. By executing the instructions included in the markup languagedocument 614, the browser application 612 displays the identifiedcontent using the format or presentation described by the markuplanguage document 614. For example, the markup language document 614includes instructions for generating and displaying a web page havingmultiple frames that include text and/or image data retrieved from theexternal system 620 and the social networking system 630. In variousembodiments, the markup language document 614 comprises a data fileincluding extensible markup language (XML) data, extensible hypertextmarkup language (XHTML) data, or other markup language data.Additionally, the markup language document 614 may include JavaScriptObject Notation (JSON) data, JSON with padding (JSONP), and JavaScriptdata to facilitate data-interchange between the external system 620 andthe user device 610. The browser application 612 on the user device 610may use a JavaScript compiler to decode the markup language document614.

The markup language document 614 may also include, or link to,applications or application frameworks such as FLASH™ or Unity™applications, the SilverLight™ application framework, etc.

In one embodiment, the user device 610 also includes one or more cookies616 including data indicating whether a user of the user device 610 islogged into the social networking system 630, which may enablemodification of the data communicated from the social networking system630 to the user device 610.

The external system 620 includes one or more web servers that includeone or more web pages 622 a, 622 b, which are communicated to the userdevice 610 using the network 650. The external system 620 is separatefrom the social networking system 630. For example, the external system620 is associated with a first domain, while the social networkingsystem 630 is associated with a separate social networking domain. Webpages 622 a, 622 b, included in the external system 620, comprise markuplanguage documents 614 identifying content and including instructionsspecifying formatting or presentation of the identified content.

The social networking system 630 includes one or more computing devicesfor a social network, including a plurality of users, and providingusers of the social network with the ability to communicate and interactwith other users of the social network. In some instances, the socialnetwork can be represented by a graph, i.e., a data structure includingedges and nodes. Other data structures can also be used to represent thesocial network, including but not limited to databases, objects,classes, meta elements, files, or any other data structure. The socialnetworking system 630 may be administered, managed, or controlled by anoperator. The operator of the social networking system 630 may be ahuman being, an automated application, or a series of applications formanaging content, regulating policies, and collecting usage metricswithin the social networking system 630. Any type of operator may beused.

Users may join the social networking system 630 and then add connectionsto any number of other users of the social networking system 630 to whomthey desire to be connected. As used herein, the term “friend” refers toany other user of the social networking system 630 to whom a user hasformed a connection, association, or relationship via the socialnetworking system 630. For example, in an embodiment, if users in thesocial networking system 630 are represented as nodes in the socialgraph, the term “friend” can refer to an edge formed between anddirectly connecting two user nodes.

Connections may be added explicitly by a user or may be automaticallycreated by the social networking system 630 based on commoncharacteristics of the users (e.g., users who are alumni of the sameeducational institution). For example, a first user specifically selectsa particular other user to be a friend. Connections in the socialnetworking system 630 are usually in both directions, but need not be,so the terms “user” and “friend” depend on the frame of reference.Connections between users of the social networking system 630 areusually bilateral (“two-way”), or “mutual,” but connections may also beunilateral, or “one-way.” For example, if Bob and Joe are both users ofthe social networking system 630 and connected to each other, Bob andJoe are each other's connections. If, on the other hand, Bob wishes toconnect to Joe to view data communicated to the social networking system630 by Joe, but Joe does not wish to form a mutual connection, aunilateral connection may be established. The connection between usersmay be a direct connection; however, some embodiments of the socialnetworking system 630 allow the connection to be indirect via one ormore levels of connections or degrees of separation.

In addition to establishing and maintaining connections between usersand allowing interactions between users, the social networking system630 provides users with the ability to take actions on various types ofitems supported by the social networking system 630. These items mayinclude groups or networks (i.e., social networks of people, entities,and concepts) to which users of the social networking system 630 maybelong, events or calendar entries in which a user might be interested,computer-based applications that a user may use via the socialnetworking system 630, transactions that allow users to buy or sellitems via services provided by or through the social networking system630, and interactions with advertisements that a user may perform on oroff the social networking system 630. These are just a few examples ofthe items upon which a user may act on the social networking system 630,and many others are possible. A user may interact with anything that iscapable of being represented in the social networking system 630 or inthe external system 620, separate from the social networking system 630,or coupled to the social networking system 630 via the network 650.

The social networking system 630 is also capable of linking a variety ofentities. For example, the social networking system 630 enables users tointeract with each other as well as external systems 620 or otherentities through an API, a web service, or other communication channels.The social networking system 630 generates and maintains the “socialgraph” comprising a plurality of nodes interconnected by a plurality ofedges. Each node in the social graph may represent an entity that canact on another node and/or that can be acted on by another node. Thesocial graph may include various types of nodes. Examples of types ofnodes include users, non-person entities, content items, web pages,groups, activities, messages, concepts, and any other things that can berepresented by an object in the social networking system 630. An edgebetween two nodes in the social graph may represent a particular kind ofconnection, or association, between the two nodes, which may result fromnode relationships or from an action that was performed by one of thenodes on the other node. In some cases, the edges between nodes can beweighted. The weight of an edge can represent an attribute associatedwith the edge, such as a strength of the connection or associationbetween nodes. Different types of edges can be provided with differentweights. For example, an edge created when one user “likes” another usermay be given one weight, while an edge created when a user befriendsanother user may be given a different weight.

As an example, when a first user identifies a second user as a friend,an edge in the social graph is generated connecting a node representingthe first user and a second node representing the second user. Asvarious nodes relate or interact with each other, the social networkingsystem 630 modifies edges connecting the various nodes to reflect therelationships and interactions.

The social networking system 630 also includes user-generated content,which enhances a user's interactions with the social networking system630. User-generated content may include anything a user can add, upload,send, or “post” to the social networking system 630. For example, a usercommunicates posts to the social networking system 630 from a userdevice 610. Posts may include data such as status updates or othertextual data, location information, images such as photos, videos,links, music or other similar data and/or media. Content may also beadded to the social networking system 630 by a third party. Content“items” are represented as objects in the social networking system 630.In this way, users of the social networking system 630 are encouraged tocommunicate with each other by posting text and content items of varioustypes of media through various communication channels. Suchcommunication increases the interaction of users with each other andincreases the frequency with which users interact with the socialnetworking system 630.

The social networking system 630 includes a web server 632, an APIrequest server 634, a user profile store 636, a connection store 638, anaction logger 640, an activity log 642, and an authorization server 644.In an embodiment of the invention, the social networking system 630 mayinclude additional, fewer, or different components for variousapplications. Other components, such as network interfaces, securitymechanisms, load balancers, failover servers, management and networkoperations consoles, and the like are not shown so as to not obscure thedetails of the system.

The user profile store 636 maintains information about user accounts,including biographic, demographic, and other types of descriptiveinformation, such as work experience, educational history, hobbies orpreferences, location, and the like that has been declared by users orinferred by the social networking system 630. This information is storedin the user profile store 636 such that each user is uniquelyidentified. The social networking system 630 also stores data describingone or more connections between different users in the connection store638. The connection information may indicate users who have similar orcommon work experience, group memberships, hobbies, or educationalhistory. Additionally, the social networking system 630 includesuser-defined connections between different users, allowing users tospecify their relationships with other users. For example, user-definedconnections allow users to generate relationships with other users thatparallel the users' real-life relationships, such as friends,co-workers, partners, and so forth. Users may select from predefinedtypes of connections, or define their own connection types as needed.Connections with other nodes in the social networking system 630, suchas non-person entities, buckets, cluster centers, images, interests,pages, external systems, concepts, and the like are also stored in theconnection store 638.

The social networking system 630 maintains data about objects with whicha user may interact. To maintain this data, the user profile store 636and the connection store 638 store instances of the corresponding typeof objects maintained by the social networking system 630. Each objecttype has information fields that are suitable for storing informationappropriate to the type of object. For example, the user profile store636 contains data structures with fields suitable for describing auser's account and information related to a user's account. When a newobject of a particular type is created, the social networking system 630initializes a new data structure of the corresponding type, assigns aunique object identifier to it, and begins to add data to the object asneeded. This might occur, for example, when a user becomes a user of thesocial networking system 630, the social networking system 630 generatesa new instance of a user profile in the user profile store 636, assignsa unique identifier to the user account, and begins to populate thefields of the user account with information provided by the user.

The connection store 638 includes data structures suitable fordescribing a user's connections to other users, connections to externalsystems 620 or connections to other entities. The connection store 638may also associate a connection type with a user's connections, whichmay be used in conjunction with the user's privacy setting to regulateaccess to information about the user. In an embodiment of the invention,the user profile store 636 and the connection store 638 may beimplemented as a federated database.

Data stored in the connection store 638, the user profile store 636, andthe activity log 642 enables the social networking system 630 togenerate the social graph that uses nodes to identify various objectsand edges connecting nodes to identify relationships between differentobjects. For example, if a first user establishes a connection with asecond user in the social networking system 630, user accounts of thefirst user and the second user from the user profile store 636 may actas nodes in the social graph. The connection between the first user andthe second user stored by the connection store 638 is an edge betweenthe nodes associated with the first user and the second user. Continuingthis example, the second user may then send the first user a messagewithin the social networking system 630. The action of sending themessage, which may be stored, is another edge between the two nodes inthe social graph representing the first user and the second user.Additionally, the message itself may be identified and included in thesocial graph as another node connected to the nodes representing thefirst user and the second user.

In another example, a first user may tag a second user in an image thatis maintained by the social networking system 630 (or, alternatively, inan image maintained by another system outside of the social networkingsystem 630). The image may itself be represented as a node in the socialnetworking system 630. This tagging action may create edges between thefirst user and the second user as well as create an edge between each ofthe users and the image, which is also a node in the social graph. Inyet another example, if a user confirms attending an event, the user andthe event are nodes obtained from the user profile store 636, where theattendance of the event is an edge between the nodes that may beretrieved from the activity log 642. By generating and maintaining thesocial graph, the social networking system 630 includes data describingmany different types of objects and the interactions and connectionsamong those objects, providing a rich source of socially relevantinformation.

The web server 632 links the social networking system 630 to one or moreuser devices 610 and/or one or more external systems 620 via the network650. The web server 632 serves web pages, as well as other web-relatedcontent, such as Java, JavaScript, Flash, XML, and so forth. The webserver 632 may include a mail server or other messaging functionalityfor receiving and routing messages between the social networking system630 and one or more user devices 610. The messages can be instantmessages, queued messages (e.g., email), text and SMS messages, or anyother suitable messaging format.

The API request server 634 allows one or more external systems 620 anduser devices 610 to call access information from the social networkingsystem 630 by calling one or more API functions. The API request server634 may also allow external systems 620 to send information to thesocial networking system 630 by calling APIs. The external system 620,in one embodiment, sends an API request to the social networking system630 via the network 650, and the API request server 634 receives the APIrequest. The API request server 634 processes the request by calling anAPI associated with the API request to generate an appropriate response,which the API request server 634 communicates to the external system 620via the network 650. For example, responsive to an API request, the APIrequest server 634 collects data associated with a user, such as theuser's connections that have logged into the external system 620, andcommunicates the collected data to the external system 620. In anotherembodiment, the user device 610 communicates with the social networkingsystem 630 via APIs in the same manner as external systems 620.

The action logger 640 is capable of receiving communications from theweb server 632 about user actions on and/or off the social networkingsystem 630. The action logger 640 populates the activity log 642 withinformation about user actions, enabling the social networking system630 to discover various actions taken by its users within the socialnetworking system 630 and outside of the social networking system 630.Any action that a particular user takes with respect to another node onthe social networking system 630 may be associated with each user'saccount, through information maintained in the activity log 642 or in asimilar database or other data repository. Examples of actions taken bya user within the social networking system 630 that are identified andstored may include, for example, adding a connection to another user,sending a message to another user, reading a message from another user,viewing content associated with another user, attending an event postedby another user, posting an image, attempting to post an image, or otheractions interacting with another user or another object. When a usertakes an action within the social networking system 630, the action isrecorded in the activity log 642. In one embodiment, the socialnetworking system 630 maintains the activity log 642 as a database ofentries. When an action is taken within the social networking system630, an entry for the action is added to the activity log 642. Theactivity log 642 may be referred to as an action log.

Additionally, user actions may be associated with concepts and actionsthat occur within an entity outside of the social networking system 630,such as an external system 620 that is separate from the socialnetworking system 630. For example, the action logger 640 may receivedata describing a user's interaction with an external system 620 fromthe web server 632. In this example, the external system 620 reports auser's interaction according to structured actions and objects in thesocial graph.

Other examples of actions where a user interacts with an external system620 include a user expressing an interest in an external system 620 oranother entity, a user posting a comment to the social networking system630 that discusses an external system 620 or a web page 622 a within theexternal system 620, a user posting to the social networking system 630a Uniform Resource Locator (URL) or other identifier associated with anexternal system 620, a user attending an event associated with anexternal system 620, or any other action by a user that is related to anexternal system 620. Thus, the activity log 642 may include actionsdescribing interactions between a user of the social networking system630 and an external system 620 that is separate from the socialnetworking system 630.

The authorization server 644 enforces one or more privacy settings ofthe users of the social networking system 630. A privacy setting of auser determines how particular information associated with a user can beshared. The privacy setting comprises the specification of particularinformation associated with a user and the specification of the entityor entities with whom the information can be shared. Examples ofentities with which information can be shared may include other users,applications, external systems 620, or any entity that can potentiallyaccess the information. The information that can be shared by a usercomprises user account information, such as profile photos, phonenumbers associated with the user, user's connections, actions taken bythe user such as adding a connection, changing user profile information,and the like.

The privacy setting specification may be provided at different levels ofgranularity. For example, the privacy setting may identify specificinformation to be shared with other users; the privacy settingidentifies a work phone number or a specific set of related information,such as, personal information including profile photo, home phonenumber, and status. Alternatively, the privacy setting may apply to allthe information associated with the user. The specification of the setof entities that can access particular information can also be specifiedat various levels of granularity. Various sets of entities with whichinformation can be shared may include, for example, all friends of theuser, all friends of friends, all applications, or all external systems620. One embodiment allows the specification of the set of entities tocomprise an enumeration of entities. For example, the user may provide alist of external systems 620 that are allowed to access certaininformation. Another embodiment allows the specification to comprise aset of entities along with exceptions that are not allowed to access theinformation. For example, a user may allow all external systems 620 toaccess the user's work information, but specify a list of externalsystems 620 that are not allowed to access the work information. Certainembodiments call the list of exceptions that are not allowed to accesscertain information a “block list”. External systems 620 belonging to ablock list specified by a user are blocked from accessing theinformation specified in the privacy setting. Various combinations ofgranularity of specification of information, and granularity ofspecification of entities, with which information is shared arepossible. For example, all personal information may be shared withfriends whereas all work information may be shared with friends offriends.

The authorization server 644 contains logic to determine if certaininformation associated with a user can be accessed by a user's friends,external systems 620, and/or other applications and entities. Theexternal system 620 may need authorization from the authorization server644 to access the user's more private and sensitive information, such asthe user's work phone number. Based on the user's privacy settings, theauthorization server 644 determines if another user, the external system620, an application, or another entity is allowed to access informationassociated with the user, including information about actions taken bythe user.

In some embodiments, the social networking system 630 can include aclustering module 646. The clustering module 646 can be implemented withthe clustering module 102.

Hardware Implementation

The foregoing processes and features can be implemented by a widevariety of machine and computer system architectures and in a widevariety of network and computing environments, FIG. 7 illustrates anexample of a computer system 700 that may be used to implement one ormore of the embodiments described herein in accordance with anembodiment of the invention. The computer system 700 includes sets ofinstructions for causing the computer system 700 to perform theprocesses and features discussed herein. The computer system 700 may beconnected (e.g., networked) to other machines. In a networkeddeployment, the computer system 700 may operate in the capacity of aserver machine or a client machine in a client-server networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. In an embodiment of the invention, the computersystem 700 may be the social networking system 630, the user device 610,and the external system 620 or a component thereof. In an embodiment ofthe invention, the computer system 700 may be one server among many thatconstitutes all or part of the social networking system 630.

The computer system 700 includes a processor 702, a cache 704, and oneor more executable modules and drivers, stored on a computer-readablemedium, directed to the processes and features described herein.Additionally, the computer system 700 includes a high performanceinput/output (I/O) bus 706 and a standard I/O bus 708. A host bridge 710couples processor 702 to high performance I/O bus 706, whereas I/O busbridge 712 couples the two buses 706 and 708 to each other. A systemmemory 714 and one or more network interfaces 716 couple to highperformance I/O bus 706. The computer system 700 may further includevideo memory and a display device coupled to the video memory (notshown). Mass storage 718 and I/O ports 720 couple to the standard I/Obus 708. The computer system 700 may optionally include a keyboard andpointing device, a display device, or other input/output devices (notshown) coupled to the standard I/O bus 708. Collectively, these elementsare intended to represent a broad category of computer hardware systems,including but not limited to computer systems based on thex86-compatible processors manufactured by Intel Corporation of SantaClara, Calif., and the x86-compatible processors manufactured byAdvanced Micro Devices (AMD), Inc., of Sunnyvale, Calif., as well as anyother suitable processor.

An operating system manages and controls the operation of the computersystem 700, including the input and output of data to and from softwareapplications (not shown). The operating system provides an interfacebetween the software applications being executed on the system and thehardware components of the system. Any suitable operating system may beused, such as the LINUX Operating System, the Apple Macintosh OperatingSystem, available from Apple Computer Inc. of Cupertino, Calif., UNIXoperating systems, Microsoft® Windows® operating systems, BSD operatingsystems, and the like. Other implementations are possible.

The elements of the computer system 700 are described in greater detailbelow. In particular, the network interface 716 provides communicationbetween the computer system 700 and any of a wide range of networks,such as an Ethernet (e.g., IEEE 802.3) network, a backplane, etc. Themass storage 718 provides permanent storage for the data and programminginstructions to perform the above-described processes and featuresimplemented by the respective computing systems identified above,whereas the system memory 714 (e.g., DRAM) provides temporary storagefor the data and programming instructions when executed by the processor702. The I/O ports 720 may be one or more serial and/or parallelcommunication ports that provide communication between additionalperipheral devices, which may be coupled to the computer system 700.

The computer system 700 may include a variety of system architectures,and various components of the computer system 700 may be rearranged. Forexample, the cache 704 may be on-chip with processor 702. Alternatively,the cache 704 and the processor 702 may be packed together as a“processor module”, with processor 702 being referred to as the“processor core”. Furthermore, certain embodiments of the invention mayneither require nor include all of the above components. For example,peripheral devices coupled to the standard I/O bus 708 may couple to thehigh performance I/O bus 706. In addition, in some embodiments, only asingle bus may exist, with the components of the computer system 700being coupled to the single bus. Moreover, the computer system 700 mayinclude additional components, such as additional processors, storagedevices, or memories.

In general, the processes and features described herein may beimplemented as part of an operating system or a specific application,component, program, object, module, or series of instructions referredto as “programs”. For example, one or more programs may be used toexecute specific processes described herein. The programs typicallycomprise one or more instructions in various memory and storage devicesin the computer system 700 that, when read and executed by one or moreprocessors, cause the computer system 700 to perform operations toexecute the processes and features described herein. The processes andfeatures described herein may be implemented in software, firmware,hardware (e.g., an application specific integrated circuit), or anycombination thereof.

In one implementation, the processes and features described herein areimplemented as a series of executable modules run by the computer system700, individually or collectively in a distributed computingenvironment. The foregoing modules may be realized by hardware,executable modules stored on a computer-readable medium (ormachine-readable medium), or a combination of both. For example, themodules may comprise a plurality or series of instructions to beexecuted by a processor in a hardware system, such as the processor 702.Initially, the series of instructions may be stored on a storage device,such as the mass storage 718. However, the series of instructions can bestored on any suitable computer readable storage medium. Furthermore,the series of instructions need not be stored locally, and could bereceived from a remote storage device, such as a server on a network,via the network interface 716. The instructions are copied from thestorage device, such as the mass storage 718, into the system memory 714and then accessed and executed by the processor 702. In variousimplementations, a module or modules can be executed by a processor ormultiple processors in one or multiple locations, such as multipleservers in a parallel processing environment.

Examples of computer-readable media include, but are not limited to,recordable type media such as volatile and non-volatile memory devices;solid state memories; floppy and other removable disks; hard diskdrives; magnetic media; optical disks (e.g., Compact Disk Read-OnlyMemory (CD ROMS), Digital Versatile Disks (DVDs)); other similarnon-transitory (or transitory), tangible (or non-tangible) storagemedium; or any type of medium suitable for storing, encoding, orcarrying a series of instructions for execution by the computer system700 to perform any one or more of the processes and features describedherein.

For purposes of explanation, numerous specific details are set forth inorder to provide a thorough understanding of the description. It will beapparent, however, to one skilled in the art that embodiments of thedisclosure can be practiced without these specific details. In someinstances, modules, structures, processes, features, and devices areshown in block diagram form in order to avoid obscuring the description.In other instances, functional block diagrams and flow diagrams areshown to represent data and logic flows. The components of blockdiagrams and flow diagrams (e.g., modules, blocks, structures, devices,features, etc.) may be variously combined, separated, removed,reordered, and replaced in a manner other than as expressly describedand depicted herein.

Reference in this specification to “one embodiment”, “an embodiment”,“other embodiments”, “one series of embodiments”, “some embodiments”,“various embodiments”, or the like means that a particular feature,design, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of, for example, the phrase “in one embodiment” or “in anembodiment” in various places in the specification are not necessarilyall referring to the same embodiment, nor are separate or alternativeembodiments mutually exclusive of other embodiments. Moreover, whetheror not there is express reference to an “embodiment” or the like,various features are described, which may be variously combined andincluded in some embodiments, but also variously omitted in otherembodiments. Similarly, various features are described that may bepreferences or requirements for some embodiments, but not otherembodiments.

The language used herein has been principally selected for readabilityand instructional purposes, and it may not have been selected todelineate or circumscribe the inventive subject matter. It is thereforeintended that the scope of the invention be limited not by this detaileddescription, but rather by any claims that issue on an application basedhereon. Accordingly, the disclosure of the embodiments of the inventionis intended to be illustrative, but not limiting, of the scope of theinvention, which is set forth in the following claims.

What is claimed is:
 1. A computer-implemented method comprising:generating, by a computing system, session information based oninformation regarding items of a plurality of item types associated withinteractions performed by active users of a social networking system;removing, by the computing system, noise in the session informationbased on a determination that one or more interactions may be a resultof automated behavior; generating, by the computing system, a graphbased on the session information, the graph comprising a plurality ofnodes and a plurality of edges connecting the plurality of nodes, eachnode of the plurality of nodes being associated with at least one item,and each edge of the plurality of edges being associated with at leastone transition probability; removing, by the computing system, one ormore edges connected to a first node of the plurality of nodes based ona determination that the first node exceeds a threshold number of edgesand further based on transition probabilities associated with the one ormore edges wherein the removing one or more edges connected to the firstnode comprises: for each node connected to the first node, multiplying atransition count associated with each edge by a constant value betweenzero and one to determine a weight for each edge, rounding each weightfor each edge to a nearest integer to determine a rounded weight foreach edge, and removing any edges with a rounded weight equal to zero;assigning, by the computing system, at least a first item of the itemsto a cluster based on similarity between the first item and the cluster,wherein the similarity is determined based on transition probabilityinformation stored in the graph; and providing, by the computing system,the cluster to a recommender system to facilitate selection of relevantinformation for potential presentation to a user.
 2. Thecomputer-implemented method of claim 1, wherein the plurality of itemtypes include at least one of other users, profiles, groups, pages,hashtags, topics, links, photos, and search terms.
 3. Thecomputer-implemented method of claim 1, wherein the session informationis based on a user or a post.
 4. The computer-implemented method ofclaim 1, further comprising: limiting the information regarding items ofa plurality of item types by at least one of a threshold number of theactive users and a threshold number of interactions associated with theactive users.
 5. The computer-implemented method of claim 1, wherein theremoving noise in the session information comprises removing noise inthe session information based on at least one of activity for more thana threshold amount of time for a session and engagement in a cycle ofinteraction.
 6. The computer-implemented method of claim 1, furthercomprising: determining a similarity score between the first item and arepresentative item of the cluster; and determining whether thesimilarity score satisfies a threshold value.
 7. Thecomputer-implemented method of claim 1, wherein the assigning at leastthe first item to the cluster comprises: determining bi-directionalagreement between the first item and a representative item of thecluster based on interactions between the first item and therepresentative item of the cluster.
 8. A system comprising: at least oneprocessor; and a memory storing instructions that, when executed by theat least one processor, cause the system to perform: generating sessioninformation based on information regarding items of a plurality of itemtypes associated with interactions performed by active users of a socialnetworking system; removing noise in the session information based on adetermination that one or more interactions may be a result of automatedbehavior; generating a graph based on the session information, the graphcomprising a plurality of nodes and a plurality of edges connecting theplurality of nodes, each node of the plurality of nodes being associatedwith at least one item, and each edge of the plurality of edges beingassociated with at least one transition probability; removing one ormore edges connected to a first node of the plurality of nodes based ona determination that the first node exceeds a threshold number of edgesand further based on transition probabilities associated with the one ormore edges, wherein the removing one or more edges connected to thefirst node comprises: for each node connected to the first node,multiplying a transition count associated with each edge by a constantvalue between zero and one to determine a weight for each edge, roundingeach weight for each edge to a nearest integer to determine a roundedweight for each edge, and removing any edges with a rounded weight equalto zero; assigning at least a first item of the items to a cluster basedon similarity between the first item and the cluster, wherein thesimilarity is determined based on transition probability informationstored in the graph; and providing the cluster to a recommender systemto facilitate selection of relevant information for potentialpresentation to a user.
 9. The system of claim 8, wherein the pluralityof item types include at least one of other users, profiles, groups,pages, hashtags, topics, links, photos, and search terms.
 10. The systemof claim 8, wherein the session information is based on a user or apost.
 11. The system of claim 8, wherein the removing noise in thesession information comprises removing noise in the session informationbased on at least one of activity for more than a threshold amount oftime for a session and engagement in a cycle of interaction.
 12. Anon-transitory computer-readable storage medium including instructionsthat, when executed by at least one processor of a computing system,cause the computing system to perform a method comprising: generatingsession information based on information regarding items of a pluralityof item types associated with interactions performed by active users ofa social networking system; removing noise in the session informationbased on a determination that one or more interactions may be a resultof automated behavior; generating a graph based on the sessioninformation, the graph comprising a plurality of nodes and a pluralityof edges connecting the plurality of nodes, each node of the pluralityof nodes being associated with at least one item, and each edge of theplurality of edges being associated with at least one transitionprobability; removing one or more edges connected to a first node of theplurality of nodes based on a determination that the first node exceedsa threshold number of edges and further based on transitionprobabilities associated with the one or more edges, wherein theremoving one or more edges connected to the first node comprises: foreach node connected to the first node, multiplying a transition countassociated with each edge by a constant value between zero and one todetermine a weight for each edge, rounding each weight for each edge toa nearest integer to determine a rounded weight for each edge, andremoving any edges with a rounded weight equal to zero; assigning atleast a first item of the items to a cluster based on similarity betweenthe first item and the cluster, wherein the similarity is determinedbased on transition probability information stored in the graph; andproviding the cluster to a recommender system to facilitate selection ofrelevant information for potential presentation to a user.
 13. Thenon-transitory computer-readable storage medium of claim 12, wherein theplurality of item types include at least one of other users, profiles,groups, pages, hashtags, topics, links, photos, and search terms. 14.The non-transitory computer-readable storage medium of claim 12, whereinthe session information is based on a user or a post.
 15. Thenon-transitory computer-readable storage medium of claim 12, wherein theremoving noise in the session information comprises removing noise inthe session information based on at least one of activity for more thana threshold amount of time for a session and engagement in a cycle ofinteraction.